code: def test_keras_convert_model_non_sequential():
      # necessary to keep the names of the layers consistent
      K.clear_session()
  
      # example from the Keras docs
      main_input = Input(
          shape=(100,),
          dtype="int32",
          name="main_input",
      )
      x = Embedding(
          output_dim=512,
          input_dim=10000,
          input_length=100,
      )(main_input)
      lstm_out = LSTM(32)(x)
      auxiliary_output = Dense(
          1,
          activation="sigmoid",
          name="aux_output",
      )(lstm_out)
      auxiliary_input = Input(
          shape=(5,),
          name="aux_input",
      )
      x = Concatenate()([lstm_out, auxiliary_input])
      x = Dense(64, activation="relu")(x)
      x = Dense(64, activation="relu")(x)
      x = Dense(64, activation="relu")(x)
      main_output = Dense(
          1,
          activation="sigmoid",
          name="main_output",
      )(x)
      model = Model(
          inputs=[main_input, auxiliary_input],
          outputs=[main_output, auxiliary_output],
      )
      wandb_model = wandb.data_types.Graph.from_keras(model)
      wandb_model_out = wandb.Graph._to_graph_json(wandb_model)
  
      print(wandb_model_out["edges"])
      assert wandb_model_out["nodes"][0] == {
          "name": "main_input",
          "id": "main_input",
          "class_name": "InputLayer",
          "output_shape": [(None, 100)],
          "num_parameters": 0,
      }
      assert wandb_model_out["edges"] == [
          ["main_input", "embedding"],
          ["embedding", "lstm"],
          ["lstm", "concatenate"],
          ["aux_input", "concatenate"],
          ["concatenate", "dense"],
          ["dense", "dense_1"],
          ["dense_1", "dense_2"],
          ["dense_2", "main_output"],
          ["lstm", "aux_output"],
      ]
  
file_path: wandb/tests/pytest_tests/unit_tests/test_keras.py
imports: import sys
  import pytest
  import wandb
  from keras import backend
  from keras.layers import LSTM, Concatenate, Dense, Embedding, Flatten, Input
  from keras.models import Model, Sequential
  from wandb.keras import WandbCallback
label_desc: Use image node_id 4 for calling a global function with example usage: test_keras_convert_model_non_sequential() without return types